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Radial basis function support vector machines

Source: R/svm_rbf.R
svm_rbf.Rd

svm_rbf() defines a support vector machine model. For classification, the model tries to maximize the width of the margin between classes using a nonlinear class boundary. For regression, the model optimizes a robust loss function that is only affected by very large model residuals and uses nonlinear functions of the predictors. The function can fit classification and regression models.

There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

¹ The default engine.

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

Usage

svm_rbf(
  mode = "unknown",
  engine = "kernlab",
  cost = NULL,
  rbf_sigma = NULL,
  margin = NULL
)

Arguments

mode

A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".

engine

A single character string specifying what computational engine to use for fitting. Possible engines are listed below. The default for this model is "kernlab".

cost

A positive number for the cost of predicting a sample within or on the wrong side of the margin

rbf_sigma

A positive number for radial basis function.

margin

A positive number for the epsilon in the SVM insensitive loss function (regression only)

Details

This function only defines what type of model is being fit. Once an engine is specified, the method to fit the model is also defined. See set_engine() for more on setting the engine, including how to set engine arguments.

The model is not trained or fit until the fit() function is used with the data.

Each of the arguments in this function other than mode and engine are captured as quosures. To pass values programmatically, use the injection operator like so:

value <- 1
svm_rbf(argument = !!value)

References

https://www.tidymodels.org, Tidy Modeling with R, searchable table of parsnip models

See also

fit(), set_engine(), update(), kernlab engine details

Examples

show_engines("svm_rbf")
#> # A tibble: 4 × 2
#>   engine    mode          
#>   <chr>     <chr>         
#> 1 kernlab   classification
#> 2 kernlab   regression    
#> 3 liquidSVM classification
#> 4 liquidSVM regression    

svm_rbf(mode = "classification", rbf_sigma = 0.2)
#> Radial Basis Function Support Vector Machine Model Specification (classification)
#> 
#> Main Arguments:
#>   rbf_sigma = 0.2
#> 
#> Computational engine: kernlab 
#>